While this will be a simplified experiment to highlight my data visualization knowledge, ultimatly I wanted to ask who pays more in Onondaga county towards the overall budget (based solely on property taxes). My theory is that per person, people and Businesses in Syracuse pay more.
Scope and Objective:
I have long been a fan of Strong Towns and they recently had a partnership with an Urban Planner I follow on YouTube to discuss property taxes and how it impacts regions. They were looking at regions with budget crises and shortfalls. Some of the visuals they used were comparable with what we learned with Data Shader, and I wanted the excuse to run a similar analysis on the property taxes from my home region, Onondaga County, with the main city being Syracuse. I want to see which regions in Onondaga county are paying for the fair share of property taxes.
My hypothesis is that the city (which in Syracuse is poorer than the suburbs) pays more than their fair share of taxes towards the region’s finances. The median salary for the county is \$61,577, and median property value is \\$152,100 while the median salary for the city is only \$38,276 and median property value for the city is \\$94,400. Although, a unique feature of Syracuse is that there are \$142,874 people who live in the city, but there are 460,528 people who live in the county meaning roughly 2:3 people will live in the suburbs. However, a significant portion of property tax revenue comes from commercial properties, and a majority are located within the city.
For this project, I will create a visualization that shows taxes across the region that can be filtered by year (2018-2020), property type (commercial, single family, multifamily), and highlight general information about city properties and suburban properties.
Eventually, I will map costs associated with the county (such as snow removal, road work, etc.) in order to get an understanding of where the return on tax dollars (ROTD) is most prevalent. Ultimately, I want to bring this to the city to help with future development within the city and county.
The data we are working with was combined from three sources. Onondaga county, both their website and a FOIL Request, as well as supplementary date from NYS. The website and FOIL Data gave me access to all the county and city taxes that each property in Onondaga county is responsible for while the NYS gave me the property values, property classes as well as the coordinates for each property.
| index | Prop Number | Municipality | Tax ID | Owner | Street Number | Street Name | key | Key Map | Index | Prop Address | Owner Name | Org Tax | City_Tax | County_Tax | Municipality Code | Municipality Name | Property Class | Property Class Description | Full Market Value | Assessment Land | Assessment Total | Grid Coordinates East | Grid Coordinates North | lat | lon | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | NaN | 312001 - Village of Camillus | 001.-01-01.0 | Hines David A | 9 | Rolling Hills Rd | 312001 | 312000001.-01-01.0 | 1.0 | 9 rolling hills rd camillus 13031-1033 | Hines David A | 1844.19 | 829.89 | 1014.30 | 312000 | Camillus | 220 | Two Family Year-Round Residence | 129900 | 31100 | 129900 | 571653 | 1107963 | 43.037073 | -77.513049 |
| 1 | 2 | NaN | 312401 - Village of North Syracuse (Town of Clay) | 001.-01-01.0 | Hochenberger Theodore A | 106 | Linda Rd | 312401 | 312400001.-01-01.0 | 25984.0 | 106 linda rd north syracuse - | Hochenberger Theodore A | 1197.96 | 539.08 | 658.88 | 312400 | Clay | 210 | One Family Year-Round Residence | 119891 | 440 | 4400 | 617693 | 1144458 | 43.138468 | -77.342089 |
| 2 | 3 | NaN | 312601 - Village of East Syracuse (Town of Dew... | 001.-01-01.0 | Bott Lawrence K | NaN | James St | 312601 | 312600001.-01-01.0 | 47965.0 | james st east syracuse - | Bott Lawrence K | 71.33 | 32.10 | 39.23 | 312600 | Dewitt | 311 | Residential Vacant Land | 8000 | 8000 | 8000 | 633053 | 1118810 | 43.068468 | -77.283742 |
| 3 | 4 | NaN | 312803 - Village of Jordan (Town of Elbridge) | 001.-01-01.0 | Cook Michael K | 127 | N Main St | 312803 | 312800001.-01-01.0 | 60551.0 | 127 main st jordan 13080-1017 | Cook Michael K | 816.18 | 367.28 | 448.90 | 312800 | Elbridge | 210 | One Family Year-Round Residence | 95053 | 8200 | 90300 | 527559 | 1119378 | 43.066929 | -77.678511 |
| 4 | 6 | NaN | 313400 - Town of Lafayette | 001.-01-01.0 | Coffin William F | NaN | LaFayette Rd | 313400 | 313400001.-01-01.0 | 73027.0 | lafayette rd lafayette - | Coffin William F | 34.57 | 15.56 | 19.01 | 313400 | LaFayette | 311 | Residential Vacant Land | 3012 | 2500 | 2500 | 627516 | 1082888 | 42.969783 | -77.303309 |
| index | Prop Number | key | Index | Org Tax | City_Tax | County_Tax | Municipality Code | Property Class | Full Market Value | Assessment Land | Assessment Total | Grid Coordinates East | Grid Coordinates North | longitude | latitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 182957.000000 | 4.156100e+04 | 182957.000000 | 141396.000000 | 1.829570e+05 | 1.750770e+05 | 1.532930e+05 | 182957.000000 | 182957.000000 | 1.829570e+05 | 1.829570e+05 | 1.829570e+05 | 182957.000000 | 1.829570e+05 | 182957.000000 | 182957.000000 |
| mean | 139818.647147 | 1.031750e+09 | 313035.360899 | 76033.269513 | 2.201628e+03 | 1.065101e+03 | 1.411210e+03 | 312984.306695 | 253.890297 | 2.305580e+05 | 3.338171e+04 | 1.803323e+05 | 597450.618615 | 1.094087e+06 | 42.999308 | -77.414023 |
| std | 80847.137863 | 5.734181e+08 | 1259.778797 | 44031.309001 | 8.354908e+03 | 3.833328e+03 | 4.996036e+03 | 1248.547641 | 118.468918 | 2.263866e+06 | 1.869484e+05 | 1.689855e+06 | 90454.631071 | 1.595547e+05 | 0.442599 | 0.319554 |
| min | 0.000000 | 1.000000e+08 | 311500.000000 | 1.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 311500.000000 | 100.000000 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000 | 0.000000e+00 | 39.963054 | -79.509326 |
| 25% | 69642.000000 | 5.100060e+08 | 312089.000000 | 37981.500000 | 6.486800e+02 | 4.680900e+02 | 6.813200e+02 | 312000.000000 | 210.000000 | 8.350800e+04 | 7.500000e+03 | 3.900000e+04 | 591366.000000 | 1.100961e+06 | 43.019095 | -77.439698 |
| 50% | 139824.000000 | 1.102000e+09 | 312689.000000 | 75722.500000 | 1.673630e+03 | 8.154600e+02 | 1.077190e+03 | 312600.000000 | 210.000000 | 1.290500e+05 | 1.570000e+04 | 1.000000e+05 | 613446.000000 | 1.114460e+06 | 43.055783 | -77.357059 |
| 75% | 210495.000000 | 1.481302e+09 | 313889.000000 | 114099.250000 | 2.556340e+03 | 1.174250e+03 | 1.521150e+03 | 313800.000000 | 220.000000 | 1.950000e+05 | 2.900000e+04 | 1.700000e+05 | 626816.000000 | 1.139382e+06 | 43.124207 | -77.306994 |
| max | 278173.000000 | 2.200004e+09 | 315689.000000 | 154954.000000 | 2.279407e+06 | 1.025733e+06 | 1.253674e+06 | 315600.000000 | 980.000000 | 4.277047e+08 | 3.960910e+07 | 3.186400e+08 | 684203.000000 | 1.191831e+06 | 43.268021 | -77.090112 |
From our data, we can see that the highest County tax will be \$1,253,647 and the lowest \\$0 with a mean of \$1,411.
Text(0.5, 1.0, 'Distribution for County taxes under 1,000$')
There are 69056 Properties that paid under $1,000 to the county, out of 182957 Properties in total ( 37.74 % of total)
Text(0.5, 1.0, 'Distribution for County taxes under 10,000$')
Text(0.5, 1.0, 'Distribution for County taxes over 10,000$')
There are 1062 Properties that paid over $10,000 to the county, out of 182957 Properties in total ( 0.58 % of total)
We can clearly see that most buildings are in Syracuse itself. But that there are 142,874 people who live in the city, but there are 460,528 people who live in the county meaning roughly 1:3 people will live in the city. Let's see if this is true with buildings.
29.42 % of properties are located in Syarcuse City
31.02 % of people live in Syracuse
Interesting, so it likes like 29% of buildings are in Syracuse despite having 31% of the people, which to me tells me there are more people living in multifamily homes than the suburbs. But to be honest it is not as much as I would have expected. My guess is that the commercial buildings make up a lot of the properties. We can check this by filtering for property classes that are for residential only.
33676
29.59 % of houses are located in Syarcuse City
Now this number makes more sense. but let's see how many people there are per house in the city vs suburbs.
On average there are 4.24 people per house in Syracuse and 4.05 people per house in the suburbs
Surprised? Syracuse is a small city with mostly single family homes as we will see below, which leads to a small increase in amount of people per house than in the suburbs.
Single Family Homes make up 88.34 % of buildings in the county
Let's see for Syracuse
Single Family Homes make up 72.57 % of buildings in the city
Two family houses + are more commonly found in the city as this shows. But we will look at the suburb view just to confirm.
Single Family Homes make up 93.0 % of buildings in the city
Before I come up with hypothesis about taxes, let's see what the averages are for city and suburb homes county tax (using single family and the same multifamily found in the city list).
Single Family suburban tax average is: $ 1382.81 Multi Family suburban tax average is: $ 3071.17 Single Family urban tax average is: $ 821.5 Multi Family urban tax average is: $ 617.76
This shows that there are is more than a 20% difference than in the city for multifamily homes. But, looking at the averages for county taxes, somehow multifamily pay less than single family in the city, and at the same time it is significantly less than suburban multifamily homes.
This might have something to do with property values, let's see what the valuations for each house type in the suburbs vs city.
| Property Class Description | Count | Total Tax | avg_as | |
|---|---|---|---|---|
| 5 | Two Family Year-Round Residence | 7015 | 456846635 | 65124.0 |
| 4 | Three Family Year-Round Residence | 790 | 61820550 | 78254.0 |
| 3 | One Family Year-Round Residence with Accessory... | 9 | 774400 | 86044.0 |
| 2 | One Family Year-Round Residence | 24439 | 1795704740 | 73477.0 |
| 1 | Multiple Residences | 204 | 14154575 | 69385.0 |
| 0 | Apartments | 1219 | 850565682 | 697757.0 |
| Property Class Description | Count | Total Tax | avg_as | |
|---|---|---|---|---|
| 5 | Two Family Year-Round Residence | 2181 | 239558933 | 109839.0 |
| 4 | Three Family Year-Round Residence | 286 | 39303000 | 137423.0 |
| 3 | One Family Year-Round Residence with Accessory... | 238 | 44796274 | 188220.0 |
| 2 | One Family Year-Round Residence | 105837 | 15532247532 | 146756.0 |
| 1 | Multiple Residences | 48 | 14389500 | 299781.0 |
| 0 | Apartments | 1512 | 732085314 | 484183.0 |
5 -44715.0 4 -59169.0 3 -102176.0 2 -73279.0 1 -230396.0 0 213574.0 Name: avg_as, dtype: float64
With the exception of Apartment Buildings, all residential properties are valued less in the city than in the suburbs, which seems to be the reason for the decreased average taxes.
So far we have compared the city to the suburbs, but let's compare it now with each municipality.
| Municipality Name | Count | Total Tax | avg_tax | |
|---|---|---|---|---|
| 3 | Dewitt | 11455 | 2.546138e+07 | 2222.731064 |
| 15 | Skaneateles | 4277 | 7.537174e+06 | 1762.257232 |
| 0 | Camillus | 10568 | 1.746192e+07 | 1652.339463 |
| 6 | Geddes | 7593 | 1.212699e+07 | 1597.127756 |
| 9 | Manlius | 14335 | 2.284781e+07 | 1593.847845 |
| 1 | Cicero | 13806 | 2.176405e+07 | 1576.420034 |
| 18 | Tully | 1519 | 2.384561e+06 | 1569.822765 |
| 2 | Clay | 21435 | 3.291280e+07 | 1535.470208 |
| 14 | Salina | 13519 | 1.946194e+07 | 1439.598725 |
| 13 | Pompey | 3620 | 4.729925e+06 | 1306.609094 |
| 16 | Spafford | 1950 | 2.462285e+06 | 1262.710174 |
| 11 | Onondaga | 9446 | 1.144500e+07 | 1211.623582 |
| 7 | LaFayette | 2617 | 3.041104e+06 | 1162.057447 |
| 8 | Lysander | 9953 | 1.150718e+07 | 1156.151836 |
| 10 | Marcellus | 3000 | 3.381800e+06 | 1127.266563 |
| 19 | Van Buren | 5987 | 6.223641e+06 | 1039.525749 |
| 12 | Otisco | 1883 | 1.739153e+06 | 923.607461 |
| 5 | Fabius | 1433 | 1.205859e+06 | 841.492673 |
| 4 | Elbridge | 2975 | 2.304642e+06 | 774.669711 |
| 17 | Syracuse | 41586 | 6.329420e+06 | 152.200732 |
On a per property level, Syracuse shows as one of the lowest tax areas for the region. But let's see if that stays true per sq ft.
| index | Municipality Name | Count | Total Tax | avg_tax | area | area feet | avg_area_tax | |
|---|---|---|---|---|---|---|---|---|
| 0 | 14 | Salina | 13519 | 1.946194e+07 | 1439.598725 | 13.75 | 72600.0 | 268.070732 |
| 1 | 6 | Geddes | 7593 | 1.212699e+07 | 1597.127756 | 9.16 | 48364.8 | 250.740023 |
| 2 | 3 | Dewitt | 11455 | 2.546138e+07 | 2222.731064 | 33.77 | 178305.6 | 142.796325 |
| 3 | 2 | Clay | 21435 | 3.291280e+07 | 1535.470208 | 47.96 | 253228.8 | 129.972594 |
| 4 | 0 | Camillus | 10568 | 1.746192e+07 | 1652.339463 | 34.44 | 181843.2 | 96.027366 |
| 5 | 9 | Manlius | 14335 | 2.284781e+07 | 1593.847845 | 49.22 | 259881.6 | 87.916224 |
| 6 | 1 | Cicero | 13806 | 2.176405e+07 | 1576.420034 | 48.28 | 254918.4 | 85.376556 |
| 7 | 17 | Syracuse | 41586 | 6.329420e+06 | 152.200732 | 24.98 | 131894.4 | 47.988540 |
| 8 | 8 | Lysander | 9953 | 1.150718e+07 | 1156.151836 | 61.71 | 325828.8 | 35.316642 |
| 9 | 15 | Skaneateles | 4277 | 7.537174e+06 | 1762.257232 | 42.61 | 224980.8 | 33.501411 |
| 10 | 11 | Onondaga | 9446 | 1.144500e+07 | 1211.623582 | 65.00 | 343200.0 | 33.347891 |
| 11 | 19 | Van Buren | 5987 | 6.223641e+06 | 1039.525749 | 35.41 | 186964.8 | 33.287767 |
| 12 | 10 | Marcellus | 3000 | 3.381800e+06 | 1127.266563 | 32.45 | 171336.0 | 19.737823 |
| 13 | 18 | Tully | 1519 | 2.384561e+06 | 1569.822765 | 25.75 | 135960.0 | 17.538694 |
| 14 | 7 | LaFayette | 2617 | 3.041104e+06 | 1162.057447 | 39.25 | 207240.0 | 14.674312 |
| 15 | 13 | Pompey | 3620 | 4.729925e+06 | 1306.609094 | 66.47 | 350961.6 | 13.477044 |
| 16 | 16 | Spafford | 1950 | 2.462285e+06 | 1262.710174 | 39.22 | 207081.6 | 11.890409 |
| 17 | 4 | Elbridge | 2975 | 2.304642e+06 | 774.669711 | 37.54 | 198211.2 | 11.627206 |
| 18 | 12 | Otisco | 1883 | 1.739153e+06 | 923.607461 | 29.53 | 155918.4 | 11.154250 |
| 19 | 5 | Fabius | 1433 | 1.205859e+06 | 841.492673 | 46.50 | 245520.0 | 4.911449 |
By Square feet, Syracuse went from being 19th and last to the middle, at 7. The two with the highest are also the smallest, and closest to the city (some of the original suburbs)
the average tax amount per sq. ft. is $ 67.47 leaving Syracuse below average and the data overall left skewed as you can see below.
<AxesSubplot:ylabel='Density'>
Let's take a look at the data as a barplot now
So we can see here that per person and per area dramatically changes the picture, let's compare quick with just houses
The interesting part of this analysis shows that while Syracuse pays the lowest per house/apartment, when looking at the taxes per sq ft, Syracuse falls in the median for both but still below mean
Now to get a sense of the overall, let's see which taxes make up the county tax base. We will classify each property code and get a total for it in each district.
| Municipality Name | Property cat desc | count | |
|---|---|---|---|
| 0 | Camillus | Agricultural | 30 |
| 1 | Camillus | Commercial | 285 |
| 2 | Camillus | Community services | 64 |
| 3 | Camillus | Industrial | 8 |
| 4 | Camillus | Public services | 186 |
As expected, a majority of the land is residential,but both Syracuse and Dewitt have a high proportion of business. And there is a lot of vacant land across the whole county.
Let's build the traditional one that will show land value and building value, then we will compare it with city, town tax
First let's get the overall look at Onondaga County (will refer to when say Syracuse). Also
This first visualization will basically show you the density for the county for reference in the other views below.
For reference, here is a diagram of Syracuse with surrounding towns
Now let's take a look at the land values as well as building values in Onondaga county.
I used a 4x4 matrix to determine which ones had the lowest values (A & D = 4, B & C = 3) (3) to the highest value (16) to determine the order for the colors. Red is the highest Building Values, while Green is the highest Land Values and white is an equal amount. (you can see the order in the color scheme below)
And let's do the same thing but this time, with County Taxes
I used a 4x4 matrix to determine which ones had the lowest values (A) to the highest value (D) to determine the order for the colors. Red is the highest Taxes Values, Grey is the second highest, White is the third highest and Green is the Lowest Tax . (you can see the order in the color scheme below)
The interesting part is that while there is a a lot of green, there is a few around the city, while a majority is actually in Liverpool, Salina, and Clay. Most of the area is white, showing that there is a fair share paid in the city and suburbs. The red area in the middle of the map is downtown, while the red areas on the edges are the most exclusive places to live in Onondaga county (Such as Skaneateles in the lower left.)